Sequential detection based classifications of rfid tags in three-dimensional space

ABSTRACT

Systems and methods for sequential detection-based classifications of radio-frequency identification (RFID) tags in three-dimensional space are provided. The methods include modeling a response from RFID tags as a probabilistic macro-channel and interrogating an RFID tag by transmitting a series of packets. Each packet is a transmit symbol and a first series of packet is a transmitted codeword. The method includes receiving, from the RFID tag, a second series of packets that is a received codeword in response to the transmitted codeword and finding a jointly typical transmit and receive codeword across all classes of macro-channels. The method also includes declaring a class of the RFID tag based on a largest likelihood between the transmitted codeword and the received codeword.

RELATED APPLICATION INFORMATION

This application claims priority U.S. Provisional Patent Application No.62/717,067, filed on Aug. 10, 2018, incorporated herein by referenceherein its entirety.

BACKGROUND Technical Field

The present invention relates to radio-frequency identification (RFID)and more particularly to detection of RFID tags.

Description of the Related Art

Passive RFID tags are getting cheaper and are readily available in avariety of configurations, sizes, read ranges, memory amounts, etc. forthe price of a few cents per tag. Even though the use of RFID tags isnot yet widespread, some stores are leveraging RFIDs in their dailyoperations and some governments have embarked on initiatives to deployRFID based check-out in the next few years. The use of RFID tags fortheft prevention has already been in place for almost a decade now,where expensive goods or small items that can be hidden or misplacedeasily are tagged and RFID readers (placed at entrance/exit doors) alertthe retailer if an item leaves the store without being already paid for.

SUMMARY

According to an aspect of the present invention, a method is providedfor sequential detection-based classifications of radio-frequencyidentification (RFID) tags in three-dimensional space. The methodincludes modeling a response from RFID tags as a probabilisticmacro-channel and interrogating an RFID tag by transmitting a series ofpackets. Each packet is a transmit symbol and a first series of packetis a transmitted codeword. The method also includes receiving, from theRFID tag, a second series of packets that is a received codeword inresponse to the transmitted codeword and finding a jointly typicaltransmit and receive codeword across all classes of macro-channels. Themethod also includes declaring a class of the RFID tag based on alargest likelihood between the transmitted codeword and the receivedcodeword.

According to another aspect of the present invention, a system isprovided for sequential detection-based classifications ofradio-frequency identification (RFID) tags in three-dimensional space.The system includes a processor device operatively coupled to a memorydevice. The processor device models a response from RFID tags as aprobabilistic macro-channel. The processor device is configured tointerrogate an RFID tag by transmitting a series of packets. Each packetis a transmit symbol and a first series of packet is a transmittedcodeword. The processor is also configured to receive, from the RFIDtag, a second series of packets that is a received codeword in responseto the transmitted codeword. The processor also finds a jointly typicaltransmit and receives codeword across all classes of macro-channels. Theprocessor is configured to declare a class of the at least one RFID tagbased on a largest likelihood between the transmitted codeword and thereceived codeword.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block diagram illustrating a radio-frequency identification(RFID) reader with a tunnel generated by the RFID reader with RFID tagspositioned in three-dimensional space with respect to the tunnel, inaccordance with the present invention;

FIG. 2 is a block diagram illustrating a high-level system forsequential detection-based classifications of RFID Tags inthree-dimensional space, in accordance with the present invention;

FIG. 3 is a block diagram illustrating a system configured to determineprobabilistic classification, in accordance with the present invention;

FIG. 4 is a block diagram illustrating a device configured to determinemacro-channel models, in accordance with the present invention;

FIG. 5 is a block diagram illustrating a device configured to implementmetric-based classification, in accordance with the present invention;

FIG. 6 is a block diagram illustrating a component configured todetermine a session concept, in accordance with the present invention;

FIG. 7 is a block diagram illustrating a component configured todetermine a metric definition, in accordance with the present invention;

FIG. 8 is a block diagram illustrating a component configured todetermine metric combinations, in accordance with the present invention;and

FIG. 9 is a flow diagram illustrating a method for sequentialdetection-based classifications of RFID Tags in three-dimensional space,in accordance with the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In accordance with aspects of the present invention, systems and methodsare provided for sequential detection-based classifications ofradio-frequency identification (RFID) tags in three-dimensional space.The methods include modeling a response from RFID tags as aprobabilistic macro-channel and interrogating an RFID tag bytransmitting a series of packets. Each packet is a transmit symbol and afirst series of packet is a transmitted codeword. The methods alsoinclude receiving, from the RFID tag, a second series of packets that isa received codeword in response to the transmitted codeword and findinga jointly typical transmit and receive codeword across all classes ofmacro-channels. The methods include declaring a class of the RFID tagbased on a largest likelihood between the transmitted codeword and thereceived codeword.

In one embodiment, checkout tunnels are designed using multiple antennasand combination of the received signal from those antennas. The exampleembodiments can accommodate the use of radio frequency (RF) absorbers,and RF reflectors to facilitate the shaping of tunnels. In exampleembodiments, a combination of received signal from different antennasalong with their characteristics such as received signal strengthindicator (RSSI), phase, doppler, etc., can be used to infer possibleposition of the RFID tag. In further example embodiments, observationsof readings of the RFID tag by different antennas over time are used toimprove the efficiency of the classifying process.

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

Each computer program may be tangibly stored in a machine-readablestorage media or device (e.g., program memory or magnetic disk) readableby a general or special purpose programmable computer, for configuringand controlling operation of a computer when the storage media or deviceis read by the computer to perform the procedures described herein. Theinventive system may also be considered to be embodied in acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform the functions describedherein.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

Referring now in detail to the figures in which like numerals representthe same or similar elements and initially to FIG. 1, a block diagramillustrating a radio-frequency identification (RFID) reader with atunnel generated by the RFID reader with RFID tags positioned inthree-dimensional space with respect to the tunnel is provided, inaccordance with example embodiments.

As shown in FIG. 1, multiple RFID tags 110 (shown in plurality as 110-1to 110-5) can be positioned in proximity to a checkout tunnel 140generated by an antenna 130 (or antennas) of an RFID reader 120. In someembodiments the RFID reader 120 can include one or more radio frequency(RF) reflectors 132 and/or RF absorbers 134. RFID tags 110 bring a wholenew set of advantages into automation of the checkout process as well asthe store management. First, since RFID tags 110 are read wirelessly,there is no need to locate and present the RFID tag 110 in a specificway to the RFID reader antenna 130. Second, multiple items (for example,multiple RFID tags 110) can be scanned at, substantially, the same timeby having them in the checkout area at once. Simultaneous reading isfrom the perception of human eye as the requirement for manual scanningof items (for example, RFID tags 110) is removed (when compared to priorsystems). The underlying wireless communication with the RFID tags 110occurs serially in a (very) short amount of time (for example, afraction of a second). Third, RFID tags 110 reduce the possibility ofhuman error since the RFID tags 110 in the vicinity of the checkoutstation (or checkout tunnel 140 or checkout bin) are read directlywithout a person presenting the tag to a reader that is the case inbarcode systems. Hence, it is not possible to present a different(wrong) RFID tag 110, or not to scan the RFID tag 110, or scan an RFIDtag 110 twice or more in error. Fourth, RFID tags 110 can mark each itemwith a unique ID (as opposed to a common ID used for the same item typein barcodes). Hence, it is easier to know exactly which item is sold andadjust the price of a similar item differently than others, for example,for a distressed or open-box merchandise. These benefits of using RFIDin the checkout process manifest itself in both self-checkout systemsusing a checkout bin, checkout tunnel 140 or regular checkout counterswhere an attendant is present.

The example embodiments provide a system in which one or more areas aredefined as (checkout) tunnels 140 and distinguish the presence of anRFID tag 110 in these areas. The checkout tunnels 140 refer to aparticular three-dimensional volume (that can be defined as a portion ofa particular open space or three-dimensional volume, as opposed to aclosed underground space). A checkout tunnel 140 is separated from therest of the three-dimensional volume by its boundary (for example, wherethe boundary is a connected surface). Part (or all) of the boundary maybe marked by actual objects such as wall, doors, etc. where some otherpart (or, in some embodiments, all) may remain open and considered to bea virtual boundary.

In the example embodiments, the design of checkout tunnels 140 by usingmultiple antennas and combination of the received signal from thosereader antennas 130. Example embodiments accommodate the use of RFabsorbers 134, and RF reflectors 132 to facilitate the shaping of suchtunnels 140. According to example embodiments, the received signal fromdifferent antennas 130 can be combined along with their characteristicssuch as received signal strength indicator (RSSI), phase, doppler, etc.and used to infer possible position of the tag. However, observing suchreadings of the RFID tag 110 by different antennas 130 over time canimprove the efficiency of the systems described herein.

In the example embodiments, a session is defined as a four-dimensionalsection marked by a limited time interval in a particular checkouttunnel 140. the example embodiments are designed to identify RFID tag110 accurately that are in different sessions. In other words, theexample embodiments identify the presence of an RFID tag 110 within acheckout tunnel 140 in a particular time interval. Multiple sessions mayoverlap over some time interval, and the example embodiments determinethe correct assignment of the RFID tags 110 to each session.Accordingly, the example embodiments handle interference from differentantennas 130 in a way that these sessions are separable.

The example embodiments separate each tunnel 140 within athree-dimensional space by itself and further address multi-sessionchallenges. The example embodiments identify every tag that is placedwithin the session and nothing else. Accordingly, some exampleembodiments handle a possible tag in vicinity of the tunnel 140 (forexample, RFID tag 110-1 and RFID tag 110-4) such that the RFID tag 110is considered out of the session (if not conclusively determined to bewithin a session). In other embodiments, RFID tags that are within apredetermined gray (or inconclusive) area around the tunnel 140 areseparately identified, and an alert is provided via the system.Additionally, the example embodiments can logically and/or physicallyseparate two different session that are running back to back on the sametunnel 140 in order to correctly identify items associated withparticular sessions.

Referring now to FIG. 2, a high-level system for sequentialdetection-based classifications of RFID Tags in three-dimensional spaceis illustratively depicted in accordance with an embodiment of thepresent invention.

Exemplary computer system (e.g., a server or a network device) forsequential detection-based classifications of RFID Tags inthree-dimensional space is shown in accordance with an embodiment of thepresent invention. The computer system 200 includes at least oneprocessing device (CPU) 205 operatively coupled to other components viaa system bus 202. A cache 206, a Read Only Memory (ROM) 208, aRandom-Access Memory (RAM) 210, an input/output (I/O) adapter 220, anetwork adapter 290, a user interface adapter 250, a cluster modelingdevice 270, a cluster management device 280 and a display adapter 260,can be operatively coupled to the system bus 202.

A first storage device 222 and a second storage device 229 can beoperatively coupled to system bus 202 by the I/O adapter 220. Thestorage devices 222 and 229 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid-state magnetic device,and so forth. The storage devices 222 and 229 can be the same type ofstorage device or different types of storage devices. Either or both ofthe storage devices 222 and 229 can be configured to operate as a datastore or database to store various logs of RFID data 272 (e.g.,session-based RFID reader data 272). The metric-based classificationdevice 270, and macro-channel modeling device 280 can include softwareand/or hardware as described herein below.

A transceiver 295 can be operatively coupled to system bus 202 bynetwork adapter 290. A display device 262 is operatively coupled tosystem bus 202 by display adapter 260. RFID (reader) data 272 can beoperatively coupled to system bus 202 directly or indirectly, forexample via metric-based classification device 270 and macro-channelmodeling device 280. The macro-channel modeling device 280 can beconfigured to define macro-channel models by explicitly describingtransition probabilities of the macro-channel models, for example asdescribed herein below with respect to FIG. 4. The metric-basedclassification device 270 can be configured to receive RFID reader data272 (for example, in real time) and find a (RFID) metric for each tagwithin an observation set for being within either of the predefinedthree-dimensional boundaries named as tunnel 140, and then use the RFIDmetric in the form of likelihood ratio to be compared with a giventhreshold in a sequential detection algorithm, for example as describedherein below with respect to FIGS. 3 to 8.

A first user input device 252 and a second user input device 259 can beoperatively coupled to system bus 202 by user interface adapter 250. Theuser input devices 252 and 259 can be any of a sensor, a keyboard, amouse, a keypad, a joystick, an image capture device, a motion sensingdevice, a power measurement device, a microphone, a device incorporatingthe functionality of at least two of the preceding devices, and soforth. Of course, other types of input devices can also be used inaccordance with the present invention. The user input devices 252 and259 can be the same type of user input device or different types of userinput devices. The user input devices 252 and 259 can be used to inputand output information to and from system 200.

Other embodiments of the present invention can optionally includefurther processing units including a graphics processing unit (“GPU”), amother board, or alternatively/additionally another storage medium, anoperating system, one or more application software, as well as includingone or more communication interfaces (e.g., RS232, Ethernet, Wi-Fi,Bluetooth, USB). Useful examples of computing devices optionallyincluded in or integrable with embodiments of the present inventioninclude, but are not limited to, personal computers, smart phones,laptops, mobile computing devices, tablet PCs, and servers. Inaccordance with embodiments of the present invention, an event recordlog source can be a computer storage medium.

Of course, the computer system 200 can also include other elements (notshown), as readily contemplated by one of skill in the art, as well asomit certain elements. For example, various other input devices and/oroutput devices can be included in computer system 200, depending uponthe particular implementation of the same, as readily understood by oneof ordinary skill in the art. For example, various types of wirelessand/or wired input and/or output devices can be used. Moreover,additional processors, controllers, memories, and so forth, in variousconfigurations can also be utilized as readily appreciated by one ofordinary skill in the art. These and other variations of the computersystem 200 are readily contemplated by one of ordinary skill in the artgiven the teachings of the present invention provided herein.

It should be understood that multiple computing devices can be operablylinked to form a computer network in a manner as to distribute and shareone or more resources, such as clustered computing devices and serverbanks/farms. In embodiments of the present invention each of theaforementioned elements (e.g., device, medium, source, or module) can bedirectly or indirectly communicably connected (e.g., via a wireless awired electronic connection) to at least one other element of thesystem. As described in more detail below, some embodiments of thepresent invention can be wholly contained within a single computingdevice. Other embodiments, however, can encompass a plurality ofinterconnected or networked devices and resources.

FIG. 3 is a block diagram illustrating a probabilistic classificationsystem 300 configured to determine probabilistic classification of RFIDtags, in accordance with example embodiments.

As shown in FIG. 3, probabilistic classification system 300 includesmetric-based classification device 270, and macro-channel modelingdevice 280 and receives RFID reader data 272 (for example, from checkouttunnels 140 and/or alternatively from stored training data sets).

The response of an RFID tag 110 is a function of the parameters andprocessing of the RFID reader 120 such as the transmit frequency,transmit power, antenna port number as well as the environmental effectssuch as reflection and absorption. Hence, the received reply by the RFIDreader 120 (for example, interrogator) by nature is probabilistic. Thismeans that even under the same set of parameters used by the RFID reader120 there are many other parameters, for example, the proximity of thenearby RFID tags 110, and orientation of the RFID tags 110 thatinfluence the generation of different replies (for (different but)similarly positioned RFID tags 110, such as, by way of illustration,RFID tags 110-2 and 110-3 in FIG. 1) including the possibility of noreply at all. For example, the reply from a first tag will be a functionof other parameters, such as an orientation (or angle) of the first tagand the presence (or existence) of nearby tags such as a second tag,third tag, etc.

In the following, the example embodiments define a macro-channel that isan abstraction of the received reply under a given interrogationscenario and specified parameters. The example embodiments then use theabstraction while implementing techniques that are used to classify thetags in different regions namely checkout tunnels 140.

The example embodiments provide RFID systems that confine wirelesssignals in order to reduce interference as well as to remove thepossibility of making error in reading an item that does not belong to acheckout bin (for example, that is contained within an area, a basket,etc., associated with a single paying entity). The example embodimentsprovide an ease of reading all items without directly scanning the itemsat once while distinguish between different items that a customer holdson their shopping basket (for example, within a checkout tunnel 140) anditems that have not been placed (for example, fully) within the checkouttunnel 140 where the customer has decided to purchase some items and hasnot made a decision about other items. The systems can be integratedwith theft prevention systems to automatically disable items (RFID tags110) from sending alerts once the items have been processed.

In some example scenarios, while a customer is at the checkout station,other merchandises (for example, RFID tags 140) exist in the proximityof the checkout station (for example, RFID tags 110-1, 110-4, and 110-5,referring back to FIG. 1 described herein above). The exampleembodiments prevent the signal from the RFID reader 120 from reachingand identifying such items (for example, RFID tags 110) as associatedwith a particular session and thus prevents disruptions to the checkoutprocess from reading undesirable (or incorrectly assigned) RFID tags110.

The example embodiments can be used to reduce interference betweenmultiple checkout stations closely positioned in one area of the store(since the reading from one checkout station might interfere with thereading from another checkout station) and thus allow for a tighterpitch between the checkout stations. The example embodiments can be usedto implement multiple checkout stations in a smaller floor space. Inaddition to preventing interference between multiple readers, theexample embodiments prevent reading tags from a nearby station in errorand counting the RFID tags 110 toward the items associated with thecurrent RFID reader 120 and checkout tunnel 140.

The example embodiments alleviate the requirement for an attendant to bepresent with RFID based checkout. The example embodiments providesimilar advantages to having an attendant with a handheld equipment witha confined reading distance, such as a handheld RFID reader (not shown).In a similar manner in which a well-trained attendant can determine aproximity required to scan the items and hence can correctly scans allthe items (maybe multiple at a time) and avoid bringing the handheldRFID reader close to the areas that the handheld RFID reader might pickup unwanted items, the example embodiments can provide defined areas andautomated feedback mechanisms to inform a customer that the RFID tags110 are within a proper area (for example, a checkout tunnel 140) forscanning. Also, example embodiments can be combined with visually markedareas in which the RFID reader 120 is enabled, for example, when theRFID reader 120 is in within a predetermined proximity of the items thatare going to be scanned (and vice versa, the customer has brought theRFID tags within a proper area for scanning).

FIG. 4 is a block diagram illustrating a component 270 configured todetermine macro-channel models, in accordance with example embodiments.

Macro-channel modeling device 270 defines the macro-channel models byexplicitly describing transition probabilities of the macro-channelmodel. Macro-channel modeling device 270 analyzes an abstraction of themacro-channel model as a discrete memoryless channel with transitionprobability f_(i)(y|x) where the channel output y is a vector of thereceived signal attributes such as received signal strength (RSSI),phase difference between the received signal and the transmitted signal,the doppler shift of the received signal, and possibly the number ofreceived packet in a given interval that constitute one macro-channeluse. The channel input x is also a vector that consists of theattributes that is used by the RFID reader 120 to interrogate the tagsuch as the antenna ports which takes its value from the set {a₁, a₂, .. . , a_(N)} for RFID system including N antenna ports, the frequencyused for interrogating the tag, and the transmit power. Please note thatthe antenna port in this context may be connected to multiple antennaelements and feed multiple antennas simultaneously, for example, as aphased array antenna 130, or it may be used to drive to a singlephysical antenna element 130. Similarly, example embodiments can definea set of antenna ports by using different beamforming vectors overmultiple antennas 130.

Using the definition of the macro-channel, macro-channel modeling device270 formulates the problem as (for example, sets a target of)transmitting a codeword x^(n) that consists of n uses of the channelrepresented as n-tuple in the form of (x₁, x₂, . . . , x_(n)) andobserving the output y^(n)=(y₁, y₂, . . . , y_(n)) in order to find whatchannel is more likely to be associated with this observation.

Macro-channel modeling device 270 formulates the process of finding themost likely channel as finding which pair of (x^(n), y^(n)) is jointlytypical over a set of channels f_(i)(y|x), i=1, 2, . . . , M for finiteset of such channels. As the codeword length grows large such jointtypical decoding incurs diminishing error when the channel transitionfunctions are distinct. Note that the problem of finding the location ofthe tag is identical to finding which channel is more likely to generatethe output y^(n) given the channel input is x^(n).

The distance between the distributions of the channel probability can bedetermined by applying Kullback-Leibler divergence (KL distance) definedas:

$\begin{matrix}{{D_{KL}( {f_{i}^{n},f_{j}^{n}} )} = {- {\sum_{y^{n}}{{f_{i}( y_{k}^{n} \middle| x^{n} )}\log {\frac{f_{j}( y^{n} \middle| x^{n} )}{f_{i}( y^{n} \middle| x^{n} )}.}}}}} & {{Equation}\mspace{14mu} ( {{Eqn}.} )\mspace{14mu} (1)}\end{matrix}$

The indexes i and j refer to two different channels, the channel i andthe channel j. f(al b) is the channel transition probability which isthe conditional probability of receiving a when b is transmitted, or theconditional probability of receiving a when the channel is excited by b.The larger the distance between the pairs of the channel transitionprobabilities the faster the convergence of the decoding algorithm asthe codeword length grows large. KL distance is a function of the chosencodeword x^(n) for the transmission. Accordingly, to optimize thesystem, the example embodiments pick up (for example, receive, identify,etc.) the transmit codeword that maximizes the minimum KL distance amongall pair of channels of interest.

The example embodiments concentrate on finite set of channels based on adetermination of channels in a neighborhood that have similar behavior(for example, based on a threshold of similarity). This similarity maybe defined, for example, based on the distance D_(KL) as describedherein above. The lower the number of the channels in the channel setf_(i)(y|x), i=1, 2, . . . , M, the easier the maximization of the KLdistance. Hence, it is easier to use smaller codeword to find thecorrect channel. In other words, there is less time used to transmitfrom different antennas to find the location of the tag (for a lowernumber of channels in the channel set).

However, at the same time, the lower the number of the channels in thechannel set f_(i)(y|x), i=1, 2, . . . , M, the lower the resolution ofthe decoding algorithm in finding the actual position of the tag.

Further, as long as the KL distance is not zero, there is a guaranteethat for long enough codewords all channels are distinguishable whichmeans that the tag location can be identified within the partitioningthat is done based on the resolution of the channels in the channel setf_(i)(y|x), i=1, 2, . . . , M.

FIG. 5 is a block diagram illustrating a metric-based classificationdevice 270 configured to implement metric-based classification, inaccordance with example embodiments.

The example embodiments apply linear codes with finite codeword lengthin communication channels instead of jointly typical (as defined ininformation theory) decoding (which can be difficult in practice).Metric-based classification device 270 can apply the same (linear codes)in a decoding process for finding the RFID tags 110 within a specificcheckout tunnel 140. In this section, the example embodiments implementsimplified processes that can work (for example, are applicable,efficient, etc.) with small number of observations. The metric-basedclassification device 270 is configured to find a metric for each RFIDtag 110 within a defined observation set for being within either of thepredefined three-dimensional boundaries named as checkout tunnels 140.The value of metric represents the possibility or probability of theRFID tag 110 being in the given checkout tunnel 140.

Metric-based classification device 270 can interpret such metrics as alikelihood of an RFID tag 110 being in a certain checkout tunnel 140.Metric-based classification device 270 can then use this likelihood inthe form of a likelihood ratio to be compared with a given threshold ina sequential detection process. The metric-based classification device270 can truncate the sequential detection process in order to avoid (forexample, intolerable, undesired, etc.) delays. In these instances,metric-based classification device 270 can modify a threshold for thesequential detection such that at each given time before a maximum timean RFID tag 110 could be assigned into one of the checkout tunnels 140(including a tunnel 140 that represents the outside RFID tags 110), aswell as staying undecided (whether to be included in a particulartunnel) for the moment. However, the final set of thresholds when themaximum time is reached must be inclusive of at least one of thecheckout tunnels 140 and the process has to be ended by such decision.

Note that a session can end before the maximum session duration isreached. For example, metric-based classification device 270 can end thesession through a trigger mechanism, for example by calculating afunction and comparing it with a threshold. Alternatively, metric-basedclassification device 270 can decide the session end as soon as all thetags for which metric-based classification device 270 had outstandingreadings have been classified.

Metric-based classification device 270 can define sessions (for example,via session concept (component) 310) for the classification of the tagsinto at least two classes, for example, inside or outside a givencheckout tunnel 140. In some embodiments additional classes can includesecondary tunnels to the checkout tunnel, for example, a price checktunnel, a returns tunnel, etc. Corresponding areas can be visuallymarked in the store indicating where items are to be placed. Accordingto example embodiments, the systems can automatically update acustomer's balance based on where different items with RFID tags 110 areplaced. Session concept 310 can also be used to define a session toclassify multiple tunnels 140 at the same time. When two separatesessions for classifications of the RFID tags 110 into, for example,classes in the set A and the classes in the set B are overlapping, itcan be difficult to separate the end time of one session from the other.This happens especially if any reading from any antenna 130 constitutesan outstanding reading for an RFID tag 110 that could have not beenclassified so far. Hence, usually concurrent sessions end almosttogether (concurrently). Also, if a session is not yet closed, the startof a new session may increase the current session duration.

According to example embodiments, in many instances, session concept 310can implement a strategy of using only two fixed sets of thresholds, onefor the times before reaching the maximum delay and the other one whenwe reach the maximum time. However, note that the thresholds for asequential detection in the example embodiments can, in some instances,be a function of time as well. Session concept 310 can use a dynamicthreshold scheme to not only satisfy the requirement of truncatedsequential detection, but also to achieve the optimal region ofconvergence (ROC) curve in terms of the optimal false alarm anddetection probabilities.

Metric-based classification device 270 can (for example, via metriccombinations (component) 330) analyze such metric functions and(determine) possible ways to combine the metrics. Before attending tothe formal definition of the metric (for example, via metric definition(component) 320) and its calculations, metric-based classificationdevice 270 defines the concept of the session (for example, via sessionconcept (component) 310) which is imperative to the definition of themetric-based classification process.

FIG. 6 is a block diagram illustrating a session concept component 310configured to implement session concepts, in accordance with exampleembodiments.

The metric-based classification is defined over a given time horizon.This time horizon is called a session. The session is usually a giventime window which has a start time and a maximum time duration. Themaximum session time is related to the maximum tolerable delay forclassification.

Therefore, in most (and in some example embodiments, all) instancessession concept 310 does not require a given length for a session andthe session may be ended anytime during the maximum allowable sessionduration. This process is for example performed thorough the sequentialdetection process discussed before.

However, the session start time 312 is an important factor in correctclassification of the RFID tags 110. The start time may be triggered byan external process or external signal that will be received by theclassification process (implemented by metric-based classificationdevice 270). The classification process then uses such session starttime 312 to calculate the metric over the time window that is causallyknown from the session start time 312 till the current time.

The classification process can classify RFID tags 110 independently overtime which means that the metric for each RFID tag 110 is dynamicallycalculated over time as the readings are received by the RFID reader120. Hence, metric-based classification device 270 performs the decisiondynamically as soon as the metric for a given RFID tag 110 crosses athreshold.

Alternatively, the classification process can use the tag dependenciesover time which means that while metric-based classification device 270does not decide to end the session, all tags will be jointly classifiedover time. In some example embodiments, after the end of a session,metric-based classification device 270 can save the parameters of theclassifications so that the new observations received later (after theend of the session) can change the classifications that were performedduring the session time. Further, in some example embodiments,observations prior to the start of the session can also be used as a wayto initialize the metric in the case of independent classification orsuch observations may be used jointly with the new observations duringthe session in order to classify the RFID tags 110. The observationsprior or after the session may come from the walk-through gate (WTG)systems and their associated antennas 130 as well as an external antenna130. The WTG can include software and hardware components, for examplethe visual signs on the floor, lights, etc.

An example of such use case is where during the checkout process while acustomer passes through the WTG (for example, corresponding to acheckout tunnel 140), metric-based classification device 270 will decideto find the content of the checkout bin within a given WTG or checkouttunnel 140. However, in instances in which a late billing is alsopossible, the RFID reader data 272 that will be available to the systembeyond the readings that were performed during the session time can, insome instances, be used to re-classify the checkout bin.

Hence, the soft values of the metric or joint metric before the harddecision in the classification process can be useful especially in thosesituations. The reason is that this metric may be used as an excerpt ofthe information gathered within the session duration and alteration ofsuch soft output is more meaningful (for example, more accurate) thansimply using the hard decisions.

The session can also have a minimum session duration 314. Suchconstraint on minimum session duration 314 provides a mechanism to avoidearly classification of an RFID tag 110 especially where the RFID tags110 are classified independently. Using minimum session duration 314,metric-based classification device 270 can ensure that theclassification is not performed before such minimum session duration314.

In some applications the session start time 312 can also be defineddynamically. According to an example embodiment, the metric-basedclassification device 270 can use a function (for example the same ordifferent metric) of the RFID readings to trigger such session starttime 312 when it crosses a threshold. An alternative implementation iswhere the session start time 312 is always a constant time before thecurrent time where the classification is performed within this constanttime window. In such cases, naturally the maximum or minimum sessiontime may not be defined. Metric-based classification device 270 can usesuch running window session to classify RFID tags 110 on the y. Anexample of such classification is the classification of the RFID tags110 in counter-top checkout or bin checkout-based systems where theitems can be freely added or removed from the checkout are (e.g., bin orcounter-top).

Finally, note that the running window defined earlier can include apositive window function that weighs the importance of each sample ormetric over time. For example, metric-based classification device 270can use a window function to add a weight to the calculation of themetric over the window to emphasize the more recent samples more thanthe older samples.

FIG. 7 is a block diagram illustrating a metric definition component 320configured to determine and implement a definition of metric, inaccordance with example embodiments.

Metric definition component 320 considers an arbitrary RFID tag 110denoted by its electronic product code (EPC) as ε. Assume that duringthe session denoted by the window time

there are W readings where the input is denoted by xi and thecorresponding output is denoted by y_(i). The input is a vector 322 thatincludes the reader transmit power, the frequency used, the antenna portand possibly other reader setting parameters and the output is thereceived signal strength (RSSI), the phase difference between thereceived signal and the transmitted signal (Phase), and the dopplershift and possibly other received signal attributes that is directlymeasured or calculated, e.g., the estimated distance of the tag from thereader.

There is a real number associated with each such reading for a giventunnel T that is defined as a metric for the reading (y_(i), x_(i)) andthe session T. The metric 324 is denoted by the function μ(T, y_(i),x_(i)). Similarly, with an abuse of notation, the metric 324 can bedefined for any pair of readings (y_(i), x_(i)) and (y_(j), x_(j)) andis denoted by μ(T, y_(i), x_(i), y_(j), x_(j)). In general the metric324 can be defined for any number of readings, e.g., n, over time asμ(T, y₁, x₁, y₂, x₂, . . . , y_(n), x_(n)).

The terminology, “singleton” refers to a single reading, for example,(y_(i), x_(i)). Similarly, the terminology “pair” refers to a pair ofreadings T, y_(i), x_(i), y_(j), x_(j)), and the metric definitioncomponent 320 uses n^(t)h order readings to refer to n readings (y₁, x₁,y₂, x₂, . . . , y_(n), x_(n)).

The dependency of the metric μ to the tunnel and any n tuple of readings(y₁, x₁, y₂, x₂, . . . , y_(n), x_(n)) can be simplified as a linearfunction that is considered as multiplication of a weight to a metricfunction that is independent of the tunnel, i.e.,

μ(T, y ₁ , x ₁ , y ₂ , x ₂ , . . . , y _(n) , x _(n))=w(T, y ₁ , x ₁ , y₂ , x ₂ , . . . , y _(n) , x _(n))m(y ₁ , x ₁ , y ₂ , x ₂ , . . . , y_(n) , x _(n))   Eqn. (2).

The metric for a tunnel T and tag ε is defined as

M(T, ε)=Σ_(i=1) ² ^(W) μ(T, y _(i) ₁ , x _(i) ₁ , y _(i) ₂ , x _(i) ₂ ,. . . , y _(i) _(n) , x _(i) _(n) )   Eqn. (3).

where 2^(W) is the power set of the set W that consists of all n-tuplesof the elements of the set S where the element of the n-tuples arealways in ascending order. M is the sum of all metrics for a given EPC ε(epsilon), for tunnel T.

According to an example embodiment, the metric definition component 320focuses metric calculations only on singletons.

M ₁(T, ε)=Σ_(i=1) ^(W)μ(T, y _(i) , x _(i))   Eqn. (4).

or extend it to include the pairs of readings as well, i.e.,

M ₁(T, ε)+M ₂(T, ε)=Σ_(i=1) ^(W)μ(T, y _(i) , x_(i))+Σ_(1≤i≤W,1≤j≤W)μ(T, y _(i) , x _(i) , y _(j) , x _(j))   Eqn. (5).

Note that the definition of M₂(T, ε) can be modified to include eachpair of antenna 130 only once, for example, out of all possible pairs(y_(i), x_(i), y_(j), x_(j)), for 1≤i, j≤W for which the antenna pair130 (a_(i), a_(j)) is similar metric definition component 320 picks onlyone pair, e.g., at random or the one which has the maximum RSSI.

In one example embodiment, metric definition component 320 furthersimplifies the weight function w(.) to be a function of the tunnel 140and the antennas 130 in the readings 1,2, . . . , n. This means that thecalculation of the metric for each tag ε is simplified in that it isenough to calculate m(.) and then find different linear combination ofm(.) to find the metric for different tunnels 140. This process can beviewed as a multiplication of a matrix by a vector where the matrixconsists of different rows for each tunnel and the vector consists of acolumn vector of all values of the function m(.) for the singleton,pairs, or possibly higher order reading vectors.

Note that the metric function M(T, ε) for all mentioned cases can becalculated dynamically using a simple recursion. Given that the value ofM(T, ε) is known up to the reading n−1, consequently the differentialchange in such metric only depends on the new reading.

The differential change due to singleton (y_(n), x_(n)) is μ(T, y_(i),x_(i)), and the differential change with respects to pair will be onlyfor the pair of readings in the form (y_(n), x_(n), y_(i), x_(i)), whichcan be computed by metric definition component 320. The same procedurecan be used for higher order readings.

FIG. 8 is a block diagram illustrating a metric combinations (component)330 configured to determine metric combinations, in accordance withexample embodiments.

Metric combinations (component) 330 can combine metrics using differentoperations. According to an example embodiment, metric combinations(component) 330 assigns the metric in the set of real numbers

and uses real number addition. According to a further exampleembodiment, metric combinations (component) 330 adds the windowingfunction θ(t−t_(i)) in the calculation of the metric, where t indicatesthe current time and t_(i) indicates the time of reading i. Hence themetric M(T, ε) may be re-written as:

M(T, ε)=Σ_((i) ₁ _(, i) ₂ _(, . . . , 1) _(n) ₎ ² ^(W) θ(t−t _(i))μ(T, y_(i) ₁ , x _(i) ₁ , y _(i) ₂ , x _(i) ₂ , . . . , y _(i) _(n) , x _(i)_(n) )   Eqn. (6).

Another possibility is to use action excited windowing function thathappens when a reading occurs. For example, when a reading from anoutside antenna 130 is received, metric combinations (component) 330 canuse an action exited windowing function to invalidate any readings froman inside antenna 130. The modification of the metric M(T, ε) due to anaction exited windowing function θ_(i)(t−t_(i)) for the action that hashappened at the time t_(i) may be re-written as.

M(T, ε)=Σ_((i) ₁ _(,i) ₂ _(, . . . , 1) _(n) ₎ ² ^(W) θ_(i)(t−t_(i))μ(T, y _(i) ₁ , x _(i) ₁ , y _(i) ₂ , x _(i) ₂ , . . . , y _(i)_(n) , x _(i) _(n) )   Eqn. (7).

Note that the action excited windowing function θ_(i)(t−t_(i)) can bedefined for the time before t_(i) or after the time t_(i) or the actionexcited windowing function can be extended in both sides before andafter the action excitation time t_(i). Also, note that this functioncan be zero in such window time duration which means that the otherreadings in this time duration are ignored. This can also be interpretedas invalidating the reading in particular time window due to some othereffects such as reading of an RFID tag 110 in particular frequency, withparticular antenna 130, and with a given range of RSSI.

In some embodiments of the current invention, a metric function can beused that is in the form of probabilities. Accordingly, metriccombinations (component) 330 can define an addition function thatpreserves the properties associated with probability values. To this endmetric combinations (component) 330 can take the summation using thesummation defined as:

μ₁⊕μ₂=μ₁+μ₂−μ₁μ₂   Eqn. (8).

When this operation is defined on a set of probabilities, for example,the real values in the interval [0,1], the operation is closed.Moreover, this operation is associative, commutative, and has identityelement, for example, 0. However, to form an Abelian group metriccombinations (component) 330 is required to extend the set to the set ofreal number less than or equal to one, for example, (−∞, 1] in order tokeep the closeness and add the inverse elements for every element.

Metric combinations (component) 330 can use this extension, for example,where a reading from an outside antenna 130 for the RFID tag 110 that isinside (a checkout tunnel 140) would add a negative value to the overallmetric and the reading from an inside antenna 130 for the same RFID tag110 adds a positive value to the overall metric. However, in terms ofprobabilities, the system can never add multiple probabilities to reacha number greater than one. So, metric combinations (component) 330treats the values of μ under such operation in (−∞,1] as the logarithmsof the probabilities or log-likelihood functions.

FIG. 9 is a flow diagram illustrating a system/method 500 for sequentialdetection-based classifications of RFID Tags in three-dimensional space,in accordance with the present invention.

At block 510, probabilistic classification system 300 models theresponse from a RFID tag 110 as a probabilistic macro-channel, forexample, as described with respect to FIG. 4 herein above. According toan example embodiment, the probabilistic macro channel depends on alocation of the RFID tags and at least one antenna. According to anotherembodiment, the probabilistic macro channel depends on a transmitantenna port, a received antenna port, and an excitation frequency.According to another embodiment, the probabilistic macro channel dependson a transmit signal power and a received signal to noise strength.According to a further embodiment, the probabilistic macro channel is afunction of a phase difference and Doppler shift between a transmitsignal and a receive signal.

At block 520, probabilistic classification system 300 interrogates aRFID tag by transmitting a series of packets where each packet isconsidered as a transmit symbol and the series of packet is thetransmitted codeword.

At block 530, probabilistic classification system 300 received a seriesof packets that is the received codeword in response to the transmittedcodeword.

At block 540, probabilistic classification system 300 finds a jointlytypical transmit and receive codeword across all classes. Probabilisticclassification system 300 may compute a likelihood between a transmittedsignal and a received signal for each class sequentially as symbols arereceived. Probabilistic classification system 300 may compute thelikelihood using a time dependent function.

At block 550, probabilistic classification system 300 declares the classof a RFID tag based on the largest likelihood between the transmit andreceive codewords. probabilistic classification system 300 can comparethe likelihood for each to a threshold to declare if the RFID tagbelongs to the class.

The foregoing is to be understood as being in every respect illustrativeand exemplary, but not restrictive, and the scope of the inventiondisclosed herein is not to be determined from the Detailed Description,but rather from the claims as interpreted according to the full breadthpermitted by the patent laws. It is to be understood that theembodiments shown and described herein are only illustrative of thepresent invention and that those skilled in the art may implementvarious modifications without departing from the scope and spirit of theinvention. Those skilled in the art could implement various otherfeature combinations without departing from the scope and spirit of theinvention. Having thus described aspects of the invention, with thedetails and particularity required by the patent laws, what is claimedand desired protected by Letters Patent is set forth in the appendedclaims.

What is claimed is:
 1. A method for sequential detection-basedclassifications of radio-frequency identification (RFID) tags inthree-dimensional space, comprising: modeling a response from RFID tagsas a probabilistic macro-channel; interrogating, by at least oneprocessor device, at least one RFID tag by transmitting a series ofpackets where each packet is a transmit symbol and a first series ofpacket is a transmitted codeword; receiving, from the at least one RFIDtag, a second series of packets that is a received codeword in responseto the transmitted codeword; finding, by the at least one processordevice, a jointly transmit and receive codeword across all classes ofmacro-channels; and declaring a class of the at least one RFID tag basedon a largest likelihood between the transmitted codeword and thereceived codeword.
 2. The method of claim 1, further comprising:determining a likelihood between two codewords based on a distance. 3.The method of claim 2, wherein the distance is Kullback Leibler (KL)distance defined as:${{D_{KL}( {f_{i}^{n},f_{j}^{n}} )} = {- {\sum_{y^{n}}{{f_{i}( y_{k}^{n} \middle| x^{n} )}\log \frac{f_{j}( y^{n} \middle| x^{n} )}{f_{i}( y^{n} \middle| x^{n} )}}}}},$where a channel output y is a vector of received signal attributes, achannel input x a vector that consists of attributes used by the atleast one processor device to interrogate the at least one RFID tag, nis a number of observations and i and j are two channels in at least onechannel set.
 4. The method of claim 1, wherein the probabilistic macrochannel depends on a location of the RFID tags and at least one antenna.5. The method of claim 1, wherein the probabilistic macro channeldepends on a transmit antenna port, a received antenna port, and anexcitation frequency.
 6. The method of claim 1, wherein theprobabilistic macro channel depends on a transmit signal power and areceived signal to noise strength.
 7. The method of claim 1, wherein theprobabilistic macro channel is a function of a phase difference andDoppler shift between a transmit signal and a receive signal.
 8. Themethod of claim 1, further comprising: computing a likelihood between atransmitted signal and a received signal for each class sequentially assymbols are received.
 9. The method of claim 8, wherein computing thelikelihood depends on an entire series of symbols that have beenreceived.
 10. The method of claim 8, wherein computing the likelihoodfurther comprises: using a time dependent function.
 11. The method ofclaim 8, wherein the likelihood for each class is compared to athreshold to declare if the at least one RFID tag belongs to the class.12. The method of claim 8, wherein a combination of two likelihoods μ₁and μ₂ is defined as:μ₁⊕μ₂=μ₁+μ₂−μ₁μ₂.
 13. A computer system for sequential detection-basedclassifications of radio-frequency identification (RFID) tags inthree-dimensional space, comprising: a processor device operativelycoupled to a memory device, the processor device being configured to:modeling a response from RFID tags as a probabilistic macro-channel;interrogate at least one RFID tag by transmitting a series of packetswhere each packet is a transmit symbol and a first series of packet is atransmitted codeword; receive, from the at least one RFID tag, a secondseries of packets that is a received codeword in response to thetransmitted codeword; find a jointly transmit and receive codewordacross all classes of macro-channels; and declare a class of the atleast one RFID tag based on a largest likelihood between the transmittedcodeword and the received codeword.
 14. The system as recited in claim13, wherein the processor device is further configured to: determine adistance between two codewords based on a Kullback-Leibler (KL)distance.
 15. The system as recited in claim 13, wherein theprobabilistic macro channel depends on a location of the RFID tags andat least one antenna.
 16. The system as recited in claim 13, wherein theprobabilistic macro channel depends on a transmit antenna port, areceived antenna port, and an excitation frequency.
 17. The system asrecited in claim 13, wherein the probabilistic macro channel depends ona transmit signal power and a received signal to noise strength
 18. Thesystem as recited in claim 13, wherein the probabilistic macro channelis a function of a phase difference and Doppler shift between a transmitsignal and a receive signal.
 19. The system as recited in claim 13,wherein the processor device is further configured to: compute alikelihood between a transmitted signal and a received signal for eachclass sequentially as symbols are received.
 20. A computer programproduct for sequential detection-based classifications ofradio-frequency identification (RFID) tags in three-dimensional space,the computer program product comprising a non-transitory computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a computing device to cause thecomputing device to perform the method comprising: modeling a responsefrom RFID tags as a probabilistic macro-channel; interrogating, by atleast one processor device, at least one RFID tag by transmitting aseries of packets where each packet is a transmit symbol and a firstseries of packet is a transmitted codeword; receiving, from the at leastone RFID tag, a second series of packets that is a received codeword inresponse to the transmitted codeword; finding, by the at least oneprocessor device, a jointly typical transmit and receive codeword acrossall classes of macro-channels; and declaring a class of the at least oneRFID tag based on a largest likelihood between the transmitted codewordand the received codeword.